CN110543426A - software performance risk detection method and device - Google Patents
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Abstract
the application provides a software performance risk detection method and device, comprising the following steps: acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set; processing the operation behaviors of the programs in the risk program set to obtain a logic characteristic vector; and carrying out risk detection by using the logic feature vector. The software performance risk detection method comprises index definition, risk identification and behavior analysis, feature storage, risk learning, operation management and the like, can prevent performance risks from influencing the operation of software, and provides guarantee for performance risk identification in the early stage of software development so as to optimize possible risk problems in the development stage.
Description
Technical Field
the application belongs to the field of computer software detection, and particularly relates to a software performance risk detection method and device based on machine learning.
Background
The performance risk is one of risks of software projects, and if the performance risk occurs to the software, the normal operation of the software may be affected, so that the software is reconstructed, the project fails, and significant loss may be brought. With the increasing complexity of business architectures and the introduction of various technical frameworks, the variety of performance risks is increasing. In the conventional technology, the identification of the performance risk is highly dependent on the experience of people, or the risk needs to be identified after the occurrence of the risk, and the identification and the elimination of the risk are not facilitated in the early stage of software development.
Disclosure of Invention
The application provides a software performance risk detection method and device, which at least solve the problem that in the prior art, software performance risks need to be identified by relying on experience of people and risks cannot be identified and eliminated in the early stage of software development.
According to one aspect of the application, a software performance risk detection method is provided, and comprises the following steps: acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set;
Processing the operation behaviors of the programs in the risk program set to obtain a logic characteristic vector;
And carrying out risk detection by using the logic feature vector.
In one embodiment, screening programs that have a performance risk according to a predefined performance index includes:
judging whether the programs in the software program set to be detected meet the performance indexes;
And intercepting the programs which do not accord with the performance indexes in the program set to be detected through dynamic audit.
in one embodiment, the processing of the operation behavior of the programs in the risk program set includes:
And analyzing the steps of the program operation behaviors in the risk program set, converting the steps of the operation behaviors into logic characteristic vectors and storing the logic characteristic vectors.
in one embodiment, risk detection using logical feature vectors includes:
clustering the logic characteristic vectors by using a learning algorithm, and classifying the logic characteristic vectors;
And judging whether the corresponding category of operation behaviors are high-risk behaviors or not according to the number of the logic feature vectors and a preset threshold.
According to another aspect of the present application, there is also provided a software performance risk detection apparatus, including:
the pre-screening unit is used for acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set;
The logic characteristic vector obtaining unit is used for processing the operation behaviors of the programs in the risk program set to obtain logic characteristic vectors;
and the risk detection unit is used for carrying out risk detection by utilizing the logic characteristic vector.
in one embodiment, the pre-screening unit comprises:
The performance judgment module is used for judging whether the programs in the software program set to be detected accord with the performance indexes;
And the intercepting module is used for intercepting the programs which do not accord with the performance indexes in the program set to be detected through dynamic audit.
In an embodiment, the logic feature vector obtaining unit specifically includes:
and the analysis and conversion module is used for analyzing the operation behavior steps of the programs in the risk program set, converting the operation behavior steps into logic characteristic vectors and storing the logic characteristic vectors.
In an embodiment, the risk detection unit comprises:
The clustering module is used for clustering the logic characteristic vectors by utilizing a learning algorithm and classifying the logic characteristic vectors;
And the high risk definition module is used for judging whether the corresponding category operation behavior is the high risk behavior according to the number of the logic characteristic vectors and a preset threshold value.
according to the method and the device, the performance risk of the software is identified by utilizing a machine learning technology, so that the function of identifying and eliminating the performance risk in the early stage of software research and development is achieved.
Drawings
in order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for software performance risk detection in the present application;
FIG. 2 is a flow chart of a method of screening programs for performance risk based on performance indicators in the present application;
FIG. 3 is a flow chart of a method for risk detection using logical feature vectors according to the present application;
FIG. 4 is a block diagram of a software performance risk detection apparatus according to the present application;
fig. 5 is a specific implementation of an electronic device in an embodiment of the present application.
Detailed Description
In the prior art, the identification of the software performance risk is highly dependent on the experience of people, or the execution of a one-to-three sheep mortalities and tonification needs to be carried out after the performance of the software has a risk problem, so that the mode is not favorable for identifying and eliminating the risk in the early stage of software development. Based on the above, the present application provides a software performance risk detection method, as shown in fig. 1, including the following steps:
S101: and acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set.
In one embodiment, the software to be tested is composed of one or more programs, and thus, each software is a program set, and the elements in the program set are one or more. The performance risk index needs to be set in advance, for example, when the usage rate of the CPU exceeds 70%, the CPU has a performance risk, and therefore, whether the usage rate of the CPU exceeds 70% is a performance risk judgment index. And screening programs in the software (program set) to be detected by using the predefined performance risk indexes, and screening the programs which do not accord with the performance risk indexes.
s102: and processing the operation behaviors of the programs in the risk program set to obtain the logic characteristic vector.
in one embodiment, the operation behavior of the program with the performance risk is disassembled into operation behavior steps and logic features contained in each step, and then the logic features are saved in a vector form.
s103: and carrying out risk detection by using the logic feature vector.
in a specific embodiment, after obtaining the logical feature vectors, all the logical feature vectors are clustered using a clustering algorithm, including but not limited to EM \ DBSCAN, K-Means, etc. The clustering result is to classify the logic feature vectors into N categories, determine the clustering result, and determine whether the behavior corresponding to the category is a high-risk behavior according to the relationship between the number of the logic feature vector elements included in the category set and a preset threshold, for example, when the number of the logic feature vector elements included in a certain category set exceeds a preset threshold (for example, 100), the operation behavior corresponding to the category is the high-risk behavior.
The execution main body of the method shown in fig. 1 can be a server, a PC, and a mobile terminal, and the method realizes the functions of pre-screening software programs according to performance indexes, screening out a program set with performance risks, analyzing the programs in the program set with performance risks, analyzing operation behavior steps and corresponding logic characteristics, and analyzing the logic characteristic vectors obtained by analysis by using a clustering algorithm to detect whether the programs are high-risk behaviors.
In one embodiment, as shown in FIG. 2, the screening of programs with performance risk according to predefined performance indicators includes the following steps:
s201: and judging whether the programs in the software program set to be detected meet the performance indexes.
s202: and intercepting the programs which do not accord with the performance indexes in the program set to be detected through dynamic audit.
In one embodiment, common performance indicators are response time, throughput, connection size, resource usage, and the like. The performance index can be defined according to different dimensions and used as an object of risk identification to judge which programs do not meet the performance requirements, and there is a performance risk. For example, taking a database server as an example, 5 performance indicators may be defined: 1. the CPU resource utilization rate is less than 70%; 2. the free memory is greater than 0; 3. the usage rate of the connection number is less than 80 percent; 4. DB CPU events are arranged in the first position of a foreground event; 5. the single SQL executes within a normal range of values.
After the performance indexes are defined, whether the programs in the program set of the software to be detected accord with the predefined performance indexes is judged item by utilizing a dynamic auditing mode, if the programs do not accord with the predefined performance indexes, the programs are intercepted and collected in a bypass or probe mode by a dynamic auditing means.
In one embodiment, it is assumed that in one database performance risk audit case, the following 4 performance indicators are hit and are not in compliance: CPU resource utilization rate exceeds 70%, the database connection number exceeds the early warning value of 80%, the first place of the foreground event is cursor: and a pin S, if the execution times of the single SQL (structured query language) is abnormal, judging that the software program set audited at this time has a performance risk, and intercepting the program with the performance risk through a probe of a middleware layer of the database to obtain a DML statement (data manipulation language) for performing multiple operations on the database.
in one embodiment, the processing of the operation behavior of the programs in the risk program set includes:
And analyzing the steps of the operation behaviors of the programs in the risk program set, converting the steps of the operation behaviors into logic characteristic vectors and storing the logic characteristic vectors.
in a specific embodiment, after a program with performance risk is intercepted, steps included in each type of operation behavior and attribute features included in each step are analyzed (for example, there are multiple iterations, nested loops, specific data tables and data fields are accessed, a long and time-consuming method is called, a large table is queried, connections are frequently obtained, and the like), for example:
analyzing and analyzing the following operation behaviors with database access by combining a risk program set, wherein the specific steps of each operation behavior are as follows:
1. Acquiring a connection- > accessing a table (data volume 10w) - > where (main key query) - > returning 1 bar;
2. acquiring a connection- > storing process- > accessing a table (data volume 100w) - > where (user-defined function nested query) - > returning 10 pieces;
3. And acquiring connection- > storing process- > accessing a table (data volume 1000w) - > writing file output.
Converting the behavior operation into the following logic characteristics described in the JSON format, and storing:
Logical feature set 1: [ { "action": getConnection "}, {" action ": querytable", "tablename": tableTest1"," tablenum ": 10w" }, { "action": querytable "," querytype ": where private key" }, { "action": return "," return num ": 1" } { "action" }
logical feature set 2: [ { "action": getconnection "}, {" action ": querypackage", "packagename": Packtest1"}, {" action ": querytable", "tablename": TableTest2"," tablenum ": 10w" }, { "action": querytable "," querytype ": where function" }, { "action": return queue "," return num ": 10" } ″
Logical feature set 3: [ { "action": getconnection "}, {" action ": querypackage", "packagename": Packtest2"}, {" action ": querytable", "tablename": TableTest3"," tablenum ": 1000w" }, { "action": writeText "," querytype ": where in" where index "} the number of bits in the table is greater than the number of bits in the table, and the number of bits in the table is greater than the number of bits in the table
In one embodiment, as shown in fig. 3, the risk detection using the logical feature vector includes:
s301: clustering the logic characteristic vectors by using a learning algorithm, and classifying the logic characteristic vectors;
s302: and defining the operation behaviors corresponding to the categories of which the number of the logic characteristic vectors exceeds the preset threshold value as high-risk behaviors.
In a specific embodiment, a K-Means clustering algorithm is taken as an example for illustration, and common machine learning algorithms that can be practically used include, but are not limited to, EM \ DBSCAN, K-Means, and the like. The algorithm can output the logic feature classes and the logic features contained in each class after inputting the vectorized logic features and the expected number of the logic feature classes (for example, 100 classes). Vectorization refers to mapping textual behaviors to vectorized mathematical space through a model for training a clustering model, and common vectorization methods include, but are not limited to, bag-of-word models, word2vec, and the like.
taking the operation behavior of database access in S102 as an example, performing cluster learning on 3 logical feature sets by using a clustering algorithm and taking 100 as an expected number of categories, 100 category sets can be obtained, and examples of category output can be described as follows:
after cluster learning is performed, a threshold N is set according to past experience, and when the number of elements in a certain category set is greater than N, the behavior corresponding to the category is defined as high-risk behavior.
The dimension of the threshold includes, but is not limited to, "the number of the category elements is greater than a specific threshold N," or "categories are sorted from most to least according to the number of the elements, and the top N categories are taken," and the value of the threshold N may be adjusted according to the learning precision.
the software performance risk detection method comprises index definition, risk identification and behavior analysis, feature storage, risk learning, operation management and the like, can be used for identifying, analyzing and controlling performance risk factors, prevents the factors from influencing the operation of software, and achieves the function of preventing the software performance risk in advance.
Based on the same inventive concept, the embodiment of the present application further provides a software performance risk detection apparatus, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Because the principle of solving the problems of the software performance risk detection device is similar to that of the software performance risk detection method, the implementation of the software performance risk detection device can refer to the implementation of the software performance risk detection method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
As shown in fig. 4, a software performance risk detection apparatus includes:
The pre-screening unit 201 is configured to acquire a software program set to be detected, and screen a program with a performance risk according to a predefined performance index to generate a risk program set;
a logic feature vector obtaining unit 202, configured to obtain a logic feature vector after processing operation behaviors of programs in the risk program set;
and a risk detection unit 203, configured to perform risk detection using the logical feature vector.
In one embodiment, the pre-filtering unit 201 includes:
the performance judgment module is used for judging whether the programs in the software program set to be detected accord with the performance indexes;
and the intercepting module is used for intercepting the programs which do not accord with the performance indexes in the program set to be detected through dynamic audit.
In an embodiment, the logic feature vector obtaining unit 202 specifically includes:
And the analysis and conversion module is used for analyzing the steps of the operation behaviors of the programs in the risk program set, converting the steps of the operation behaviors into logic characteristic vectors and storing the logic characteristic vectors.
In an embodiment, the risk detection unit 203 comprises:
The clustering module is used for clustering the logic characteristic vectors by utilizing a learning algorithm and classifying the logic characteristic vectors;
And the high risk defining module is used for defining the operation behaviors corresponding to the categories of which the number of the logic characteristic vectors exceeds the preset threshold value as high risk behaviors.
The application simultaneously provides a software performance risk detection device, can provide guarantee to the performance risk identification of software research and development early stage to just optimize the risk problem that probably exists at the research and development stage.
the method and the device have the advantages that the program to be detected is analyzed and split, the program to be detected is split into operation behavior steps, the split steps are subjected to clustering analysis in a logic characteristic vector mode, whether the program is a high-risk behavior or not is judged according to the result of the clustering analysis, and the function of detecting the software performance risk in the early stage of software development is achieved.
The principle and the implementation mode of the present application are explained by applying specific embodiments in the present application, and the description of the above embodiments is only used to help understanding the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
An embodiment of the present application further provides a specific implementation manner of an electronic device capable of implementing all steps in the method in the foregoing embodiment, and referring to fig. 5, the electronic device specifically includes the following contents:
a processor (processor)301, a memory 302, a communication Interface 303, a bus 304, and a nonvolatile memory 305;
The processor 301, the memory 302 and the communication interface 303 complete mutual communication through the bus 304;
The processor 301 is configured to call the computer programs in the memory 302 and the non-volatile memory 305, and the processor executes the computer programs to implement all the steps of the method in the above embodiments, for example, when the processor executes the computer programs to implement the following steps:
s101: and acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set.
S102: and processing the operation behaviors of the programs in the risk program set to obtain the logic characteristic vector.
S103: and carrying out risk detection by using the logic feature vector.
Embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and the computer program when executed by a processor implements all the steps of the method in the above embodiments, for example, the processor implements the following steps when executing the computer program:
S101: and acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set.
S102: and processing the operation behaviors of the programs in the risk program set to obtain the logic characteristic vector.
s103: and carrying out risk detection by using the logic feature vector.
the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment. Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein. The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification.
In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.
Claims (10)
1. A software performance risk detection method is characterized by comprising the following steps:
acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set;
processing the operation behaviors of the programs in the risk program set to obtain logic characteristic vectors;
and utilizing the logic characteristic vector to carry out risk detection.
2. the method of claim 1, wherein the screening of programs with a performance risk according to a predefined performance index comprises:
Judging whether the programs in the software program set to be detected meet the performance index;
And intercepting the programs which do not accord with the performance indexes in the program set to be detected through dynamic audit.
3. the detection method according to claim 1, wherein the processing of the operation behavior of the programs in the risk program set comprises:
and analyzing the operation behavior steps of the programs in the risk program set, converting the operation behavior steps into the logic characteristic vectors and storing the logic characteristic vectors.
4. The detection method according to claim 1, wherein the risk detection using the logical feature vector comprises:
clustering the logic characteristic vectors by using a learning algorithm;
and judging whether the corresponding category of operation behaviors are high-risk behaviors or not according to the number of the logic feature vectors and a preset threshold.
5. A software performance risk detection apparatus, comprising:
the pre-screening unit is used for acquiring a software program set to be detected, screening programs with performance risks according to predefined performance indexes, and generating a risk program set;
A logic characteristic vector obtaining unit, configured to obtain a logic characteristic vector after processing operation behaviors of the programs in the risk program set;
And the risk detection unit is used for carrying out risk detection by utilizing the logic characteristic vector.
6. The detection apparatus according to claim 5, wherein the pre-screening unit comprises:
The performance judgment module is used for judging whether the programs in the software program set to be detected accord with the performance indexes;
And the intercepting module is used for intercepting the programs which do not accord with the performance indexes in the program set to be detected through dynamic audit.
7. the apparatus according to claim 5, wherein the logical eigenvector obtaining unit specifically includes:
and the analysis and conversion module is used for analyzing the operation behavior steps of the programs in the risk program set, converting the operation behavior steps into the logic characteristic vectors and storing the logic characteristic vectors.
8. the detection apparatus according to claim 5, wherein the risk detection unit comprises:
the clustering module is used for clustering the logic characteristic vectors by utilizing a learning algorithm;
And the high risk definition module is used for judging whether the corresponding category operation behavior is the high risk behavior according to the number of the logic characteristic vectors and a preset threshold value.
9. an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the software performance risk detection method of any one of claims 1 to 4 when executing the program.
10. a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the software performance risk detection method of any one of claims 1 to 4.
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| CN112527620A (en) * | 2020-12-24 | 2021-03-19 | 北京百度网讯科技有限公司 | Database performance analysis method and device, electronic equipment, medium and product |
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